我们介绍了一种新颖的屏蔽图AutoEncoder（MGAE）框架，以在图形结构数据上执行有效的学习。从自我监督学习中欣识见，我们随机掩盖了大部分边缘，并在训练期间尝试重建这些缺失的边缘。 Mgae有两个核心设计。首先，我们发现掩蔽了输入图结构的高比率，例如70 \％$，产生一个非凡和有意义的自我监督任务，使下游应用程序受益。其次，我们使用图形神经网络（GNN）作为编码器，以在部分掩蔽的图表上执行消息传播。为了重建大量掩模边缘，提出了一种定制的互相关解码器。它可以捕获多粒度的锚边的头部和尾部节点之间的互相关。耦合这两种设计使MGAE能够有效且有效地培训。在多个开放数据集（Planetoid和OGB基准测试）上进行了广泛的实验，证明MGAE通常比链接预测和节点分类更好地表现优于最先进的无监督竞争对手。

Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}

近年来，自我监督学习（SSL）已广泛探索。特别是，生成的SSL在自然语言处理和其他AI领域（例如BERT和GPT的广泛采用）中获得了新的成功。尽管如此，对比度学习 - 严重依赖结构数据的增强和复杂的培训策略，这是图SSL的主要方法，而迄今为止，生成SSL在图形上的进度（尤其是GAES）尚未达到潜在的潜力。正如其他领域所承诺的。在本文中，我们确定并检查对GAE的发展产生负面影响的问题，包括其重建目标，训练鲁棒性和错误指标。我们提出了一个蒙版的图形自动编码器Graphmae，该图可以减轻这些问题，以预处理生成性自我监督图。我们建议没有重建图形结构，而是提议通过掩盖策略和缩放余弦误差将重点放在特征重建上，从而使GraphMae的强大训练受益。我们在21个公共数据集上进行了大量实验，以实现三个不同的图形学习任务。结果表明，Graphmae-A简单的图形自动编码器具有仔细的设计-CAN始终在对比度和生成性最新基准相比，始终产生优于性的表现。这项研究提供了对图自动编码器的理解，并证明了在图上的生成自我监督预训练的潜力。

链接预测是一项重要的任务，在各个域中具有广泛的应用程序。但是，大多数现有的链接预测方法都假定给定的图遵循同质的假设，并设计基于相似性的启发式方法或表示学习方法来预测链接。但是，许多现实世界图是异性图，同义假设不存在，这挑战了现有的链接预测方法。通常，在异性图中，有许多引起链接形成的潜在因素，并且两个链接的节点在一个或两个因素中往往相似，但在其他因素中可能是不同的，导致总体相似性较低。因此，一种方法是学习每个节点的分离表示形式，每个矢量捕获一个因子上的节点的潜在表示，这铺平了一种方法来模拟异性图中的链接形成，从而导致更好的节点表示学习和链接预测性能。但是，对此的工作非常有限。因此，在本文中，我们研究了一个新的问题，该问题是在异性图上进行链接预测的分离表示学习。我们提出了一种新颖的框架分解，可以通过建模链接形成并执行感知因素的消息来学习以促进链接预测来学习解开的表示形式。在13个现实世界数据集上进行的广泛实验证明了Disenlink对异性恋和血友病图的链接预测的有效性。我们的代码可从https://github.com/sjz5202/disenlink获得

图形对比学习（GCL）已成为学习图形无监督表示的有效工具。关键思想是通过数据扩展最大化每个图的两个增强视图之间的一致性。现有的GCL模型主要集中在给定情况下的所有图表上应用\ textit {相同的增强策略}。但是，实际图通常不是单态，而是各种本质的抽象。即使在相同的情况下（例如，大分子和在线社区），不同的图形可能需要各种增强来执行有效的GCL。因此，盲目地增强所有图表而不考虑其个人特征可能会破坏GCL艺术的表现。 {a} u Mentigation（GPA），通过允许每个图选择自己的合适的增强操作来推进常规GCL。本质上，GPA根据其拓扑属性和节点属性通过可学习的增强选择器为每个图定制了量身定制的增强策略，该策略是插件模块，可以通过端到端的下游GCL型号有效地训练。来自不同类型和域的11个基准图的广泛实验证明了GPA与最先进的竞争对手的优势。此外，通过可视化不同类型的数据集中学习的增强分布，我们表明GPA可以有效地识别最合适的数据集每个图的增强基于其特征。

Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a homophily assumption to learn embeddings with the guidance of supervisory signals, leaving the unsupervised heterophilous scenario relatively unexplored. This problem becomes especially relevant in fields where a scarcity of labels exists. Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily. Specifically, we design a dual-channel feature embedding pipeline to discriminate r-ego networks using node attributes and structural information separately. We employ heterophily adapted self-supervised learning objective functions to optimise the framework to learn intrinsic node embeddings. We show that SELENE's components improve the quality of node embeddings, facilitating the discrimination of connected heterophilous nodes. Comprehensive empirical evaluations on both synthetic and real-world datasets with varying homophily ratios validate the effectiveness of SELENE in homophilous and heterophilous settings showing an up to 12.52% clustering accuracy gain.

数据增强已广泛用于图像数据和语言数据，但仍然探索图形神经网络（GNN）。现有方法专注于从全局视角增强图表数据，并大大属于两个类型：具有特征噪声注入的结构操纵和对抗训练。但是，最近的图表数据增强方法忽略了GNNS“消息传递机制的本地信息的重要性。在这项工作中，我们介绍了本地增强，这通过其子图结构增强了节点表示的局部。具体而言，我们将数据增强模拟为特征生成过程。鉴于节点的功能，我们的本地增强方法了解其邻居功能的条件分布，并生成更多邻居功能，以提高下游任务的性能。基于本地增强，我们进一步设计了一个新颖的框架：La-GNN，可以以即插即用的方式应用于任何GNN模型。广泛的实验和分析表明，局部增强一致地对各种基准的各种GNN架构始终如一地产生性能改进。

Although substantial efforts have been made using graph neural networks (GNNs) for AI-driven drug discovery (AIDD), effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules. Recent studies suggest that big GNN models pre-trained by self-supervised learning on unlabeled datasets enable better transfer performance in downstream molecular property prediction tasks. However, they often require large-scale datasets and considerable computational resources, which is time-consuming, computationally expensive, and environmentally unfriendly. To alleviate these limitations, we propose a novel pre-training model for molecular representation learning, Bi-branch Masked Graph Transformer Autoencoder (BatmanNet). BatmanNet features two tailored and complementary graph autoencoders to reconstruct the missing nodes and edges from a masked molecular graph. To our surprise, BatmanNet discovered that the highly masked proportion (60%) of the atoms and bonds achieved the best performance. We further propose an asymmetric graph-based encoder-decoder architecture for either nodes and edges, where a transformer-based encoder only takes the visible subset of nodes or edges, and a lightweight decoder reconstructs the original molecule from the latent representation and mask tokens. With this simple yet effective asymmetrical design, our BatmanNet can learn efficiently even from a much smaller-scale unlabeled molecular dataset to capture the underlying structural and semantic information, overcoming a major limitation of current deep neural networks for molecular representation learning. For instance, using only 250K unlabelled molecules as pre-training data, our BatmanNet with 2.575M parameters achieves a 0.5% improvement on the average AUC compared with the current state-of-the-art method with 100M parameters pre-trained on 11M molecules.

图形神经网络（GNNS）在建模图形结构数据方面表明了它们的能力。但是，实际图形通常包含结构噪声并具有有限的标记节点。当在这些图表中培训时，GNN的性能会显着下降，这阻碍了许多应用程序的GNN。因此，与有限标记的节点开发抗噪声GNN是重要的。但是，这是一个相当有限的工作。因此，我们研究了在具有有限标记节点的嘈杂图中开发鲁棒GNN的新问题。我们的分析表明，嘈杂的边缘和有限的标记节点都可能损害GNN的消息传递机制。为减轻这些问题，我们提出了一种新颖的框架，该框架采用嘈杂的边缘作为监督，以学习去噪和密集的图形，这可以减轻或消除嘈杂的边缘，并促进GNN的消息传递，以缓解有限标记节点的问题。生成的边缘还用于规则地将具有标记平滑度的未标记节点的预测规范化，以更好地列车GNN。实验结果对现实世界数据集展示了在具有有限标记节点的嘈杂图中提出框架的稳健性。

异质图卷积网络在解决异质网络数据的各种网络分析任务方面已广受欢迎，从链接预测到节点分类。但是，大多数现有作品都忽略了多型节点之间的多重网络的关系异质性，而在元路径中，元素嵌入中关系的重要性不同，这几乎无法捕获不同关系跨不同关系的异质结构信号。为了应对这一挑战，这项工作提出了用于异质网络嵌入的多重异质图卷积网络（MHGCN）。我们的MHGCN可以通过多层卷积聚合自动学习多重异质网络中不同长度的有用的异质元路径相互作用。此外，我们有效地将多相关结构信号和属性语义集成到学习的节点嵌入中，并具有无监督和精选的学习范式。在具有各种网络分析任务的五个现实世界数据集上进行的广泛实验表明，根据所有评估指标，MHGCN与最先进的嵌入基线的优势。

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8x faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.

图表可以模拟实体之间的复杂交互，它在许多重要的应用程序中自然出现。这些应用程序通常可以投入到标准图形学习任务中，其中关键步骤是学习低维图表示。图形神经网络（GNN）目前是嵌入方法中最受欢迎的模型。然而，邻域聚合范例中的标准GNN患有区分\ EMPH {高阶}图形结构的有限辨别力，而不是\ EMPH {低位}结构。为了捕获高阶结构，研究人员求助于主题和开发的基于主题的GNN。然而，现有的基于主基的GNN仍然仍然遭受较少的辨别力的高阶结构。为了克服上述局限性，我们提出了一个新颖的框架，以更好地捕获高阶结构的新框架，铰接于我们所提出的主题冗余最小化操作员和注射主题组合的新颖框架。首先，MGNN生成一组节点表示W.R.T.每个主题。下一阶段是我们在图案中提出的冗余最小化，该主题在彼此相互比较并蒸馏出每个主题的特征。最后，MGNN通过组合来自不同图案的多个表示来执行节点表示的更新。特别地，为了增强鉴别的功率，MGNN利用重新注射功能来组合表示的函数w.r.t.不同的主题。我们进一步表明，我们的拟议体系结构增加了GNN的表现力，具有理论分析。我们展示了MGNN在节点分类和图形分类任务上的七个公共基准上表现出最先进的方法。

最近，图形神经网络（GNNS）在各种现实情景中获得了普及。尽管取得了巨大成功，但GNN的建筑设计严重依赖于体力劳动。因此，自动化图形神经网络（Autopmn）引起了研究界的兴趣和关注，近年来显着改善。然而，现有的autopnn工作主要采用隐式方式来模拟并利用图中的链接信息，这对图中的链路预测任务不充分规范化，并限制了自动启动的其他图表任务。在本文中，我们介绍了一个新的Autognn工作，该工作明确地模拟了缩写为autogel的链接信息。以这种方式，AutoGel可以处理链路预测任务并提高Autognns对节点分类和图形分类任务的性能。具体地，AutoGel提出了一种新的搜索空间，包括层内和层间设计中的各种设计尺寸，并采用更强大的可分辨率搜索算法，以进一步提高效率和有效性。基准数据集的实验结果展示了自动池上的优势在几个任务中。

Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also face some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate augmented data into models by randomly masking parts of the input. However, some open problems of random dropping on GNNs remain to be solved. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, augmented data introduced to GNNs causes the incomplete coverage of parameters and unstable training process. Third, there is no theoretical analysis on the effectiveness of random dropping methods on GNNs. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the propagated messages during the message-passing process. More importantly, we find that DropMessage provides a unified framework for most existing random dropping methods, based on which we give theoretical analysis of their effectiveness. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, enabling it become a theoretical upper bound of other methods. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has the advantages of both effectiveness and generalization, and can significantly alleviate the problems mentioned above.

Inferring missing links or detecting spurious ones based on observed graphs, known as link prediction, is a long-standing challenge in graph data analysis. With the recent advances in deep learning, graph neural networks have been used for link prediction and have achieved state-of-the-art performance. Nevertheless, existing methods developed for this purpose are typically discriminative, computing features of local subgraphs around two neighboring nodes and predicting potential links between them from the perspective of subgraph classification. In this formalism, the selection of enclosing subgraphs and heuristic structural features for subgraph classification significantly affects the performance of the methods. To overcome this limitation, this paper proposes a novel and radically different link prediction algorithm based on the network reconstruction theory, called GraphLP. Instead of sampling positive and negative links and heuristically computing the features of their enclosing subgraphs, GraphLP utilizes the feature learning ability of deep-learning models to automatically extract the structural patterns of graphs for link prediction under the assumption that real-world graphs are not locally isolated. Moreover, GraphLP explores high-order connectivity patterns to utilize the hierarchical organizational structures of graphs for link prediction. Our experimental results on all common benchmark datasets from different applications demonstrate that the proposed method consistently outperforms other state-of-the-art methods. Unlike the discriminative neural network models used for link prediction, GraphLP is generative, which provides a new paradigm for neural-network-based link prediction.

Existing graph contrastive learning methods rely on augmentation techniques based on random perturbations (e.g., randomly adding or dropping edges and nodes). Nevertheless, altering certain edges or nodes can unexpectedly change the graph characteristics, and choosing the optimal perturbing ratio for each dataset requires onerous manual tuning. In this paper, we introduce Implicit Graph Contrastive Learning (iGCL), which utilizes augmentations in the latent space learned from a Variational Graph Auto-Encoder by reconstructing graph topological structure. Importantly, instead of explicitly sampling augmentations from latent distributions, we further propose an upper bound for the expected contrastive loss to improve the efficiency of our learning algorithm. Thus, graph semantics can be preserved within the augmentations in an intelligent way without arbitrary manual design or prior human knowledge. Experimental results on both graph-level and node-level tasks show that the proposed method achieves state-of-the-art performance compared to other benchmarks, where ablation studies in the end demonstrate the effectiveness of modules in iGCL.

Variational Graph Autoencoders (VGAEs) are powerful models for unsupervised learning of node representations from graph data. In this work, we systematically analyze modeling node attributes in VGAEs and show that attribute decoding is important for node representation learning. We further propose a new learning model, interpretable NOde Representation with Attribute Decoding (NORAD). The model encodes node representations in an interpretable approach: node representations capture community structures in the graph and the relationship between communities and node attributes. We further propose a rectifying procedure to refine node representations of isolated notes, improving the quality of these nodes' representations. Our empirical results demonstrate the advantage of the proposed model when learning graph data in an interpretable approach.

图形神经网络（GNNS）在学习的图形结构化数据中显示了包含节点/边缘特征信息的图表，应用于社交网络，推荐，欺诈检测和知识图形推理。在这方面，过去已经提出了各种策略，以改善GNN的富有效率。例如，一个简单的选项是通过扩展HID-DIN维度或增加GNN层的数量来简单地增加参数大小。然而，更宽的隐藏层可以容易地导致过度拟合，逐步添加更多GNN层可能导致过度的过度。在本文中，我们提出了一种模型 - 不可知方法，即图形神经网络（NGNN）中的网络，允许任意GNN模型通过使模型更深的模型来提高模型容量。然而，除了添加或加宽GNN层，NGNN通过在每个GNN层内插入非线性前馈神经网络层来加深GNN模型。在OGBN - 产品数据上应用于Graphsage基础GNN的NGNN分析表明它可以将模型保持对节点特征或图形结构扰动的稳定性。此外，对节点分类和链路预测任务的广泛评估结果表明，NGNN在不同的GNN架构上可靠地工作。对于实例，它可以提高OGBN-Products上的GraphSage的测试精度，并提高了@ 100分数ogbl-ppa上的密封率为7.08％，并且Propsage + Edge-Attr在ogbl-ppi上的Hits @ 20分数达到6.22％。在此提交时，它实现了OGB链路预测排行榜上的两个第一个位置。

我们展示了拓扑转型等值表示学习，是图形数据节点表示的自我监督学习的一般范式，以实现图形卷积神经网络（GCNNS）的广泛适用性。通过在转换之前和之后的拓扑转换和节点表示之间的相互信息，从信息理论的角度来看，我们将提出的模型正式化。我们得出最大化这种相互信息可以放宽以最小化应用拓扑变换与节点表示之间的估计之间的跨熵。特别是，我们寻求从原始图表中采样节点对的子集，并在每对之间翻转边缘连接以改变图形拓扑。然后，我们通过从原始和变换图的特征表示重构拓扑转换来自动列出表示编码器以学习节点表示。在实验中，我们将所提出的模型应用于下游节点分类，图形分类和链路预测任务，结果表明，所提出的方法优于现有的无监督方法。

图形神经网络（GNN）已被广泛应用于各种领域，以通过图形结构数据学习。在各种任务（例如节点分类和图形分类）中，他们对传统启发式方法显示了显着改进。但是，由于GNN严重依赖于平滑的节点特征而不是图形结构，因此在链接预测中，它们通常比简单的启发式方法表现出差的性能，例如，结构信息（例如，重叠的社区，学位和最短路径）至关重要。为了解决这一限制，我们建议邻里重叠感知的图形神经网络（NEO-GNNS），这些神经网络（NEO-GNNS）从邻接矩阵中学习有用的结构特征，并估算了重叠的邻域以进行链接预测。我们的Neo-Gnns概括了基于社区重叠的启发式方法，并处理重叠的多跳社区。我们在开放图基准数据集（OGB）上进行的广泛实验表明，NEO-GNNS始终在链接预测中实现最新性能。我们的代码可在https://github.com/seongjunyun/neo_gnns上公开获取。